v0.3.0

A correctness release: broader checks against Python Prophet 1.4.0, and the fixes they turned up.
Evaluation scores now match Python's exactly, which removes a few options and fields, so read
Removed before upgrading.

Added

• FittingError has a memory-limit reason for models too large for WebAssembly's 4 GiB of
  memory.

Changed

• Flat growth fits the way Python does. Every flat model uses Prophet's Stan optimizer and
  accepts map.changepoints and map.changepointPriorScale. Models saved by earlier versions
  still load and predict.
• Large Fourier orders fit quickly, and models with more than 2048 parameters, which used to
  fail, now fit as they do in Python.
• Histories the model can fit exactly, such as noiseless data, fit with a noise scale near
  zero, as in Python, instead of failing.
• Standardized regressors use exactly the mean and standard deviation Python computes.
• performanceMetrics matches Python's performance_metrics exactly, so some scores change,
  most of all rolling scores. rows scores are sorted by horizon, as in Python.
• mape fails with zero-actual when an actual is within 1e-8 of zero, not only at exactly
  zero.
• decodeEvaluationReport rejects holdout reports saved by earlier versions, because their scores
  are recomputed when decoded.

Fixed

• Regressors that are not standardized no longer fail validation when their values are too large
  to average.

Removed

• The noise-collapse reason of FittingError, which no fit returns any more.
• The mapeZeroActual option of performanceMetrics, searchModels and evaluateHoldout, with
  the includedCount and excludedCount score fields. To skip zero actuals, leave those rows out
  before scoring.
• rowCount on rolling metric points, which always equals the report's windowRows. They now
  have their own RollingMetricPoint type.
• The empty-bucket reason of EvaluationMetricError, which can no longer happen.

Verification against Python Prophet

• Property-based tests run in every CI build and check that hostile input produces typed
  errors, never crashes.
• A weekly differential comparison fits hundreds of newly generated inputs with both
  libraries and reports every case where Effect Prophet fits worse, with a seed that replays it.
  Cases where Python's own result is unstable are reported separately. Run it locally with
  npm run differential.
• New fixtures cover regressor standardization, large Fourier orders and evaluation metrics.

Full Changelog: v0.2.0...v0.3.0